Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation

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Designing effective mitigation strategies against influenza outbreak requires an accurate prediction of a disease's future course of spreading. Real time information such as syndromic surveillance data and influenza-like-illness (ILI) reports by clinicians can be used to generate estimates of the current state of spreading of a disease. Syndromic surveillance data are immediately available, in contrast to ILI reports that require data collection and processing. On the other hand, they are less credible than ILI data because they are essentially behavioral responses from a community. In this paper, we present a method to combine immediately-available-but-less-reliable syndromic surveillance data with reliable-but-time-delayed ILI data. This problem is formulated as a non-linear stochastic filtering problem, and solved by a particle filtering method. Our experimental results from hypothetical pandemic scenarios show that state estimation is improved by utilizing both sets of data compared to when using only one set. However, the amount of improvement depends on the relative credibility and length of delay in ILI data. An analysis for a linear, Gaussian case is presented to support the results observed in the experiments.
Publisher
SPRINGER
Issue Date
2016-06
Language
English
Article Type
Article
Keywords

PANDEMIC INFLUENZA; TRACKING EPIDEMICS; OUTBREAKS; MODELS

Citation

FLEXIBLE SERVICES AND MANUFACTURING JOURNAL, v.28, no.1-2, pp.233 - 253

ISSN
1936-6582
DOI
10.1007/s10696-014-9204-0
URI
http://hdl.handle.net/10203/207585
Appears in Collection
IE-Journal Papers(저널논문)
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